In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM) and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD) and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR) algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.
Feature fusion is an important part of building high-precision convolutional neural networks. In the field of image classification, though widely used in processing multiscale features of the same layer and short connections in the same receptive field, feature fusion is rarely used in long connection operations across receptive fields. In order to fuse the high- and low-level features of image classification, a feature fusion module SCFF (selective cross-layer feature fusion) for long connections is designed in this work. The SCFF can connect the long-distance feature maps in different receptive fields in a top-down order and apply the self-attention mechanism to fuse them two by two. The final fusion result is used as the input of the classifier. In order to verify the effectiveness of the model, the image classification experiment was done on a number of typical datasets. The experimental results prove that the model can fit the existing convolutional neural network well and effectively improve the classification accuracy of the convolutional network only at the cost of a small amount of calculation.
1) Background/IntroductionBy selectively enhancing the features extracted from convolution networks, the attention mechanism has shown its effectiveness for low-level visual tasks, especially for image super-resolution (SR). However, due to the spatiotemporal continuity of video sequences, simply applying image attention to a video cannotdoes not seem to obtain good SR results. At present, there is still a lack of suitable attention structure to achieve efficient video SR. 2) MethodsIn this work, building uponbased on the correlation exploration for the dual attention, i.e., -position attention and channel attention, we proposed deep dual attention, underpinned by equipped with self-attention alignment (DASAA), for video SR.Specifically, Firstly, we start by constructing a dual attention module (DAM) to strengthen the acquired spatiotemporal features and adopt a self-attention structure with the morphological mask to achieve attention alignment. Then, on top of based on the attention features, we utilize the up-sampling operation to reconstruct the super-resolved video images, and introduce the LSTM (long short time memory) network to guarantee the coherent consistency of the generated video frames both in temporal and spatial domains temporally and spatially. 3) ResultsExperimental results and comparisons on the actual Youku-VESR dataset and the typical benchmark dataset-Vimeo-90k demonstrate that our proposed approach not only achieves the best video SR effect while but also takinges the least amount of computation. Specifically, in the Youku-VESR dataset, our proposed approach achieves a test PSNR of over PSNR/SSIM metrics is 35.290db and a SSIM of 0.939, respectively. In the Vimeo-90k dataset, the PSNR/SSIM indexes of our approach are individually 32.878db and 0.774. Moreover, the FLOPS (float-point operations per second) of our approach is as low as 6.39G. 4) ConclusionsThe proposed DASAA method surpasses all video SR algorithms in the comparison outperforms all the compared algorithms for video SR. It is also revealedproved that there is no linear relationship between positional attention and channel attention. It 2 suggests that our DASAA with LSTM coherent consistency architecture may have great potential for many low-level vision video applications.
Resolution decrease and motion blur are two typical image degradation processes that are usually addressed by deep networks, specifically convolutional neural networks (CNNs). However, since real images are usually obtained through multiple degradations, the vast majority of current CNN methods that employ a single degradation process inevitably need to be improved to account for multiple degradation effects. In this work, motivated by degradation decoupling and multiple-order attention drop-out gating, we propose a joint deep recovery model to efficiently address motion blur and resolution reduction simultaneously. Our degradation decoupling style improves the continence and the efficiency of model construction and training. Moreover, the proposed multi-order attention mechanism comprehensively and hierarchically extracts multiple attention features and fuses them properly by drop-out gating. The proposed approach is evaluated using diverse benchmark datasets including natural and synthetic images. The experimental results show that our proposed method can efficiently complete joint motion blur and image super-resolution (SR).
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